Deep Reinforcement learning (RL) [1]}, [2]}, [3]}, [4]} offers the potential to improve motion planning performance [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. One method of using Deep RL in motion planning is to generate end-to-end trajectories for systems with complex dynamics and constraints. This leverages a key benefit of Deep RL, which is its ability to perform well for problems where a nonlinear programming formulation is not suitably defined. Similarly, Deep RL can provide benefit when the underlying problem is stochastic [11]}. Deep RL has recently been applied in aerospace domains. Deep RL is applied to spacecraft orbit guidance in [12]} and to planetary descent in [13]} on a six degree of freedom system model. In both works, rewards are carefully shaped to produce desirable behavior. In [14]} Deep RL is applied to a spacecraft docking problem. The recent work of [15]} applies Deep RL for adaptive optimal flight control.
